Building Community-Centred NLP Resources for Puno Quechua
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Abstract (arXiv)
The preservation of under-resourced languages requires digital tools and resources shaped by and for their speakers. We present the first dedicated ASR resources for Puno Quechua (ISO 639-3: qxp): (1) the largest speech corpus for any single Quechua variety, consisting in 66 hours of recordings for scripted and spontaneous speech (including 36 hours of manually transcribed and validated data), collected via a participatory design campaign; (2) the first systematic ASR benchmark for Puno Quechua, evaluating state-of-the-art models and fine-tuning Whisper-base, wav2vec2-base, and XLS-R-300M, with and without continued pre-training (CPT); (3) an open release of all datasets and fine-tuned models.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 14 references that are in the map. First posted on arXiv 27 May 2026.
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References · 4
- 2021XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scalecited by 601
- 2022Improving Low-Resource Speech Recognition with Pretrained Speech Models: Continued Pretraining vs. Semi-Supervised Trainingcited by 6
- 2026Continued Pretraining for Low-Resource Swahili ASR: Achieving State-of-the-Art Performance with Minimal Labeled Datacited by 2
- 2025Quechua Speech Datasets in Common Voice: The Case of Puno Quechuacited by 1
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